Thresholding (image processing): Difference between revisions

Revision as of 12:38, 25 April 2024

Revision as of 12:43, 25 April 2024

Line 2:

[[Image:Pavlovsk Railing of bridge Yellow palace Winter.jpg|thumb|250px|Original image.]][[Image:Pavlovsk Railing of bridge Yellow palace Winter bw threshold.jpg|thumb|250px|The binary image resulting from a thresholding of the original image.]]

In [[digital image processing]], '''thresholding''' is the simplest method of [[image segmentation|segmenting images]]. From a [[grayscale]] image, thresholding can be used to create [[binary image]]s.<ref>[[#Shapiro2001{{cite book |(last1=Shapiro, et|first1=Linda alG. |last2=Stockman |first2=George C. |title=Computer Vision |date=2001: |publisher=Prentice Hall |isbn=978-0-13-030796-5 |page=83)]] }}</ref>

==Definition==

Line 12:

While in some cases, the threshold <math>T</math> can be selected manually by the user, there are many cases where the user wants the threshold to be automatically set by an algorithm. In those cases, the threshold should be the "best" threshold in the sense that the partition of the pixels above and below the threshold should match as closely as possible the actual partition between the two classes of objects represented by those pixels (e.g., pixels below the threshold should correspond to the background and those above to some objects of interest in the image).

Many types of automatic thresholding methods exist, the most famous and widely used being [[Otsu's method]]. The following list, based on the works of [[#Sezgin2004|Sezgin et al. (2004)]] categorizescategorized thresholding methods into broad groups based on the information the algorithm manipulates.<ref>{{cite journal |last1=Sankur |first1=Bülent |title=Survey over image thresholding techniques and quantitative performance evaluation |journal=Journal of Electronic Imaging |date=2004 |volume=13 |issue=1 |pages=146 |doi=10.1117/1.1631315 |bibcode=2004JEI....13..146S }}</ref> Note however that such a categorization is necessarily fuzzy as some methods can fall in several categories (for example, Otsu's method can be both considered a histogram-shape and a clustering algorithm)

* '''[[Histogram]] shape'''-based methods, where, for example, the peaks, valleys and curvatures of the smoothed histogram are analyzed.<ref>{{Cite journal |last1=Zack |first1=G W |last2=Rogers |first2=W E |last3=Latt |first3=S A |date=July 1977 |title=Automatic measurement of sister chromatid exchange frequency |journal=Journal of Histochemistry & Cytochemistry |language=en |volume=25 |issue=7 |pages=741–753 |doi=10.1177/25.7.70454 |pmid=70454 |s2cid=15339151 |doi-access=free }}</ref> Note that these methods, more than others, make certain assumptions about the image intensity probability distribution (i.e., the shape of the histogram),

Line 59:

==References==

{{Reflist}}

==Sources==

*<cite id=Pham2007>{{cite journal |last1=Pham |first1=Nhu-An |last2=Morrison |first2=Andrew |last3=Schwock |first3=Joerg |last4=Aviel-Ronen |first4=Sarit |last5=Iakovlev |first5=Vladimir |last6=Tsao |first6=Ming-Sound |last7=Ho |first7=James |last8=Hedley |first8=David W |title=Quantitative image analysis of immunohistochemical stains using a CMYK color model |journal=Diagnostic Pathology |date=December 2007 |volume=2 |issue=1 |page=8 |doi=10.1186/1746-1596-2-8 |doi-access=free |pmid=17326824 |pmc=1810239 }}</cite>

*<cite id=Shapiro2001> [[Linda Shapiro|Shapiro, Linda G.]] & Stockman, George C. (2002). "Computer Vision". Prentice Hall. {{ISBN|0-13-030796-3}}</cite>

*<cite id=Sezgin2004>{{cite journal |last1=Sankur |first1=Bu¨lent |title=Survey over image thresholding techniques and quantitative performance evaluation |journal=Journal of Electronic Imaging |date=2004 |volume=13 |issue=1 |pages=146 |doi=10.1117/1.1631315 |bibcode=2004JEI....13..146S }}</cite>

==Further reading==